CN110010987A - A kind of remaining charging time prediction technique of the electric car based on big data - Google Patents
A kind of remaining charging time prediction technique of the electric car based on big data Download PDFInfo
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- CN110010987A CN110010987A CN201910292007.8A CN201910292007A CN110010987A CN 110010987 A CN110010987 A CN 110010987A CN 201910292007 A CN201910292007 A CN 201910292007A CN 110010987 A CN110010987 A CN 110010987A
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
- H01M10/44—Methods for charging or discharging
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
- H01M10/48—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte
- H01M10/488—Cells or batteries combined with indicating means for external visualization of the condition, e.g. by change of colour or of light density
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
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- Chemical & Material Sciences (AREA)
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- Charge And Discharge Circuits For Batteries Or The Like (AREA)
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Abstract
The remaining charging time prediction technique of the electric car based on big data that the invention discloses a kind of.Method are as follows: when starting to charge, characteristic information is uploaded to big data system by automobile, and prediction model carries out remaining charging time prediction, full of remaining time and was charged to for 80% remaining time to vehicle feedback；In charging process, add up the charging time；After charging complete, the true charging time is uploaded to big data system by vehicle；Monthly from big data system, garbled data sample, it is training dataset, validation data set A and validation data set B by sample random division, utilize the multiple machine learning models of training sample training, trained model is verified using validation data set A and validation data set B, according to verification result overall merit, optimal prediction model is chosen.The present invention improves the accuracy of the remaining charging time prediction of electric car, improves the experience of user, enhances the competitiveness of electric car.
Description
Technical field
It is especially a kind of based on the electronic of big data the present invention relates to the technical field that new energy BMS and big data are combined
Automobile residue charging time prediction technique.
Background technique
Battery management system BMS, is the brain of new-energy automobile control, and automobile can be safely, effectively using key.At present
The all technical of BMS the next mature, but still very coarse to charging time forecast function.Common remaining time is pre-
Survey method simply estimates charging remaining time based on SOC, charging current, and this method is too simple, and error is larger.
Charging time forecasting accuracy is ignored by producer, there are two reason is main: one is charging remaining time and function
Safety is unrelated, and also there is no embody accuracy in national standard；The second is other than SOC and charging current, cell degradation degree,
Battery temperature, ambient temperature, fast and slow charge strategy, many factors such as consistency of battery SOC can have an impact the charging time, but
Be how these factors influence, effect have it is much, be difficult with experiment mode obtain as a result, therefore can not establish effectively
Function prediction model.
Summary of the invention
The purpose of the present invention is to provide a kind of accuracy, and the electric car residue based on big data high, that timeliness is strong is filled
Electric time forecasting methods.
The technical solution for realizing the aim of the invention is as follows: a kind of remaining charging time of the electric car based on big data is pre-
Survey method, comprising the following steps:
Step 1, when starting to charge, characteristic information is uploaded to big data system by automobile, and prediction model carries out remaining charging
Time prediction full of remaining time and was charged to for 80% remaining time to vehicle feedback；
In step 2, charging process, add up the charging time；After charging complete, the true charging time is uploaded to big number by vehicle
According to system；
Step 3, monthly from big data system, sample random division is training dataset, verifying by garbled data sample
Data set A and validation data set B utilizes validation data set A and verifying number using the multiple machine learning models of training sample training
Trained model is verified according to collection B, and optimal prediction model is chosen according to verification result overall merit.
Further, when starting to charge described in step 1, characteristic information is uploaded to big data system by automobile, predicts mould
Type carries out remaining charging time prediction, full of remaining time and is charged to for 80% remaining time to vehicle feedback, specific as follows:
Step 1.1, when just start to charge when, utilize BMS system acquisition battery information, ambient condition information；
Step 1.2, by characteristic information battery SOC, cycle-index, battery temperature, ambient temperature, fast charge or the mark of trickle charge
Position uploads to cloud；
Step 1.3, the form for processing data into mode input；
Step 1.4, is charged to SOC=80% required time at remaining time of charging by optimum prediction model prediction；
The result of prediction is passed to battery management system by step 1.5, is shown by battery management system.
Further, in charging process described in step 2, add up the charging time；After charging complete, vehicle will really charge
Time uploads to big data system, specific as follows:
In step 2.1, charging process, BMS adds up the charging time；
After step 2.2, charging complete, by the characteristic information at the initial stage of charging, including battery SOC, cycle-index, battery temperature
Degree, ambient temperature, fast and slow charge flag bit, and be charged to 80% actual time, actual time for being full of, upload to big number
According to system.
Further, described in step 3 monthly from big data system, sample random division is by garbled data sample
Training dataset, validation data set A and validation data set B utilize verifying using the multiple machine learning models of training sample training
Data set A and validation data set B verifies trained model and chooses optimal prediction model according to verification result overall merit,
It is specific as follows:
Step 3.1, monthly according to data training prediction model；
Step 3.2 screens the data of upload, data within choosing 1 year, by down-sampling or over-sampling mode,
Ensure that data are distributed in different cycle-index sections inner equilibrium；
Using screening module, data the year before are rejected first, the data bulk in different cycle-index sections are counted, to not
With section by the way of down-sampling or over-sampling, so that training data equiblibrium mass distribution on cycle-index characteristic dimension；
Step 3.3 handles sample data, and random division was training set and verifying collection A, while from nearest one month
Data in, extract a certain proportion of data at random as verifying collection B；
Step 3.4, using the multiple models of training data training, training set is respectively adopted and is carried out in verifying collection A and verifying collection B
Verifying, calculates its accuracy Ea, Eb, obtains final accuracy E=0.5*Ea+0.5*Eb according to identical weight proportion；
The final judgment criteria of step 3.5, accuracy of selection E as optimum prediction model.
Compared with prior art, the present invention its remarkable advantage is: (1) information of different vehicle can be incorporated into one
It rises, without carrying out laboratory test to automobile, the prediction in remaining charging time under varying environment can be carried out；(2) pass through screening
Module equiblibrium mass distribution on cycle-index scale by data, is conducive to model training effect；(3) in different time periods according to two
Verify data comprehensive assessment, to select optimum prediction model, it is ensured that the timeliness and accuracy of remaining time prediction model；
(4) remaining time prediction accuracy is high, improves the experience of user, enhances the competitiveness of automobile.
Detailed description of the invention
Fig. 1 is that the present invention is based on the streams that the charging time is predicted in the remaining charging time prediction technique of the electric car of big data
Journey schematic diagram.
Fig. 2 is the flow diagram that the charging time is reported in the present invention.
Fig. 3 is the flow diagram that charge model is selected in the present invention.
Fig. 4 is the flow diagram of data screening in the present invention.
Specific embodiment
The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.
In conjunction with Fig. 1, Fig. 2, Fig. 3, a kind of remaining charging time prediction technique of electric car based on big data of the present invention, packet
Include following steps:
Step 1, in conjunction with Fig. 1, when starting to charge, characteristic information is uploaded to big data system by automobile, and prediction model carries out
Remaining charging time prediction, full of remaining time and was charged to for 80% remaining time to vehicle feedback, specific as follows:
Step 1.1, when just start to charge when, utilize BMS system acquisition battery information, ambient condition information；
Step 1.2, by characteristic information battery SOC, cycle-index, battery temperature, ambient temperature, fast charge or the mark of trickle charge
Position uploads to cloud；
Step 1.3, the form for processing data into prediction model input；
Step 1.4, is charged to SOC=80% required time at remaining time of charging by optimum prediction model prediction；
The result of prediction is passed to battery management system by step 1.5, when showing remaining charging by battery management system
Between.
Step 2, in conjunction with Fig. 2, in charging process, add up the charging time；After charging complete, vehicle will be on the true charging time
Big data system is passed to, it is specific as follows
In step 2.1, charging process, BMS adds up the charging time；
After step 2.2, charging complete, by the characteristic information at the initial stage of charging, including battery SOC, cycle-index, battery temperature
Degree, ambient temperature, fast and slow charge flag bit, and be charged to 80% actual time, the actual time being full of etc. and upload to and is big
Data system.
Step 3, in conjunction with Fig. 3, monthly from big data system, garbled data sample, by sample random division be training number
Validation data set is utilized using the multiple machine learning models of training sample training according to collection, validation data set A and validation data set B
A and validation data set B verifies trained model and chooses optimal prediction model, specifically such as according to verification result overall merit
Under:
Step 3.1, the accuracy in order to guarantee model, monthly according to a data prediction model of training；
Step 3.2, in conjunction with Fig. 4, the data of upload are screened, with down-sampling or over-sampling is crossed, so that being followed in difference
Data balancing is distributed in ring time intervals；
Using battery SOC, cycle-index, battery temperature, ambient temperature, fast charge or the flag bit of trickle charge as input, charging
To 20% required time, full of required time as output, inputoutput data is handled；
Data random division in 1 year is training sample, verifying sample A by step 3.3, from a nearest month data
Certain proportion is randomly choosed, sample B is verified；
Step 3.4 trains multiple machine learning models using training set, using trained model, collects respectively in verifying
A, it is verified in B, calculates separately its accuracy Ea, Eb, obtain the accuracy E=for assessment models according to identical weight proportion
0.5*Ea+0.5*Eb, specific as follows:
Machine learning model is inputted, the relationship between output data by data training.For training machine mould
Type, it is thus necessary to determine that input, output variable；
Input variable
SOC: indicating battery dump energy, and unit is %, indicates that inside battery electricity accounts for the ratio of total electricity；
Cycle-index: and cell degradation has direct relation, the battery after emptying is full of, being then vented this process again is
One cycle, battery is from factory to end-of-life, and cycle-index is close, which can characterize battery aging status；
Battery temperature: battery temperature can have an impact battery capacity, in addition when temperature is very low, need first to preheat
It is recharged to certain temperature, preheating process also can elapsed time；
Ambient temperature: battery can carry out heat interaction with extraneous, and ambient temperature can have an impact battery temperature；
Fast and slow charge flag bit: the fast charge of battery, trickle charge are two kinds of charging strategies of battery charging, and automobile is in when charging,
Smaller electric current is trickle charge strategy；When charging on charging pile, larger electric current is fast charge strategy.When automobile dispatches from the factory, fast, trickle charge plan
Slightly be it is fixed, i.e., its charging current, drop current course be fixed.
Output variable
The remaining charging time: battery is since charging, to the fully charged required time；
Be charged to for 80% remaining time: battery is since charging, to being charged to the time required for SOC=80%；
In electric car charging process, either fast charge or trickle charge, charging current is all non-constant, when being charged to SOC=
When 80%, electric current initially enters drop current course, and charge rate reduces after dropping electric current, generally from SOC=80% to full of required
Time accounts for more than half of total charging time, therefore will be charged to the time required when 80% as an output point；
The final judgment criteria of step 3.5, accuracy of selection E as optimum prediction model.
In conclusion the present invention can combine the information of different vehicle, without carrying out laboratory survey to automobile
Examination can carry out the prediction in remaining charging time under varying environment；It is by screening module that data are equal on cycle-index scale
Weighing apparatus distribution, is conducive to model training effect；According to two verify data comprehensive assessments in different time periods, to select optimum prediction
Model, it is ensured that the timeliness and accuracy of remaining time prediction model；Remaining time prediction accuracy is high, improves user's
Experience, enhances the competitiveness of automobile.
Claims (4)
1. a kind of remaining charging time prediction technique of the electric car based on big data, which comprises the following steps:
Step 1, when starting to charge, characteristic information is uploaded to big data system by automobile, and prediction model carries out the remaining charging time
Prediction full of remaining time and was charged to for 80% remaining time to vehicle feedback；
In step 2, charging process, add up the charging time；After charging complete, the true charging time is uploaded to big data system by vehicle
System；
Step 3, monthly from big data system, sample random division is training dataset, verify data by garbled data sample
Collect A and validation data set B, using the multiple machine learning models of training sample training, utilizes validation data set A and validation data set
B verifies trained model and chooses optimal prediction model according to verification result overall merit.
2. the remaining charging time prediction technique of the electric car based on big data according to claim 1, which is characterized in that
When starting to charge described in step 1, characteristic information is uploaded to big data system by automobile, and prediction model carries out the remaining charging time
Prediction full of remaining time and was charged to for 80% remaining time to vehicle feedback, specific as follows:
Step 1.1, when just start to charge when, utilize BMS system acquisition battery information, ambient condition information；
Step 1.2, will be on characteristic information battery SOC, cycle-index, battery temperature, ambient temperature, fast charge or the flag bit of trickle charge
Pass to cloud；
Step 1.3, the form for processing data into mode input；
Step 1.4, is charged to SOC=80% required time at remaining time of charging by optimum prediction model prediction；
The result of prediction is passed to battery management system by step 1.5, is shown by battery management system.
3. the remaining charging time prediction technique of the electric car based on big data according to claim 1, which is characterized in that
In charging process described in step 2, add up the charging time；After charging complete, the true charging time is uploaded to big data by vehicle
System, specific as follows:
In step 2.1, charging process, BMS adds up the charging time；
After step 2.2, charging complete, by the characteristic information at the initial stage of charging, including it is battery SOC, cycle-index, battery temperature, outer
Boundary's temperature, fast and slow charge flag bit, and be charged to 80% actual time, actual time for being full of, upload to big data system
System.
4. the remaining charging time prediction technique of the electric car based on big data according to claim 1, which is characterized in that
Described in step 3 monthly from big data system, sample random division is training dataset, verifying number by garbled data sample
Validation data set A and verify data are utilized using the multiple machine learning models of training sample training according to collection A and validation data set B
Collection B verifies trained model and chooses optimal prediction model according to verification result overall merit, specific as follows:
Step 3.1, monthly according to data training prediction model；
Step 3.2 screens the data of upload, and data within choosing 1 year pass through down-sampling or over-sampling mode, it is ensured that
Data are distributed in different cycle-index sections inner equilibrium；
Using screening module, data the year before are rejected first, the data bulk in different cycle-index sections are counted, to not same district
Between by the way of down-sampling or over-sampling so that training data equiblibrium mass distribution on cycle-index characteristic dimension；
Step 3.3 handles sample data, and random division is training set and verifying collection A, while from nearest one month number
In, a certain proportion of data are extracted at random as verifying collection B；
Step 3.4, using the multiple models of training data training, training set is respectively adopted and is tested in verifying collection A and verifying collection B
Card, calculates its accuracy Ea, Eb, obtains final accuracy E=0.5*Ea+0.5*Eb according to identical weight proportion；
The final judgment criteria of step 3.5, accuracy of selection E as optimum prediction model.
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Cited By (11)
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| CN110703101A (en) * | 2019-09-12 | 2020-01-17 | 北京交通大学 | Lithium ion battery inter-partition cycle capacity decline prediction method |
| CN110723029A (en) * | 2019-09-27 | 2020-01-24 | 东软睿驰汽车技术（沈阳）有限公司 | Method and device for determining charging strategy |
| CN112068004A (en) * | 2020-09-16 | 2020-12-11 | 北京嘀嘀无限科技发展有限公司 | Method and device for determining battery abnormity and battery charging remaining time |
| CN112215434A (en) * | 2020-10-22 | 2021-01-12 | 深圳市加码能源科技有限公司 | LSTM model generation method, charging duration prediction method and medium |
| CN112329336A (en) * | 2020-10-22 | 2021-02-05 | 同济大学 | Method for planning charging-cooling process of battery pack of electric vehicle |
| CN113119796A (en) * | 2021-03-29 | 2021-07-16 | 上海交通大学 | Electric vehicle residual charging time prediction method and system based on cloud sparse charging data |
| CN113147506A (en) * | 2021-04-25 | 2021-07-23 | 北京新能源汽车股份有限公司 | Big data-based vehicle-to-vehicle mutual learning charging remaining time prediction method and device |
| CN113595164A (en) * | 2020-04-30 | 2021-11-02 | 华为技术有限公司 | Method and device for charging management and control |
| CN114879045A (en) * | 2022-03-29 | 2022-08-09 | 中国第一汽车股份有限公司 | Method, device, terminal and storage medium for testing verification of charging remaining time |
| CN115020842A (en) * | 2022-06-27 | 2022-09-06 | 东莞新能安科技有限公司 | Charging time length determining method, device, equipment and product |
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| CN113147506B (en) * | 2021-04-25 | 2022-05-06 | 北京新能源汽车股份有限公司 | Big data-based vehicle-to-vehicle mutual learning charging remaining time prediction method and device |
| WO2023052685A1 (en) | 2021-09-29 | 2023-04-06 | Kempower Oyj | Forecasting charging time of electric vehicles |
| CN114879045A (en) * | 2022-03-29 | 2022-08-09 | 中国第一汽车股份有限公司 | Method, device, terminal and storage medium for testing verification of charging remaining time |
| CN115020842A (en) * | 2022-06-27 | 2022-09-06 | 东莞新能安科技有限公司 | Charging time length determining method, device, equipment and product |
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